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共 716 条记录，以下是561-570 订阅

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data-driven Soft Sensing of Polyoxymethylene Melt Index using Takagi-Sugeno Fuzzy Neural Network and Quantum Genetic Algorithm

Data-Driven Soft Sensing of Polyoxymethylene Melt Index usin...

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data driven control and learning systems (ddcls)

作者： Guangji Yu Haiquan Zhao Yangyang Liang Yong Gao Weiyong Zhang Malika Sader Methanol Branch Ningxia Coal Co. Ltd. Yinchuan China Tsingyun Control (Beijing) Technologies Co. Ltd Beijing China Tsingyun Intelligence (Beijing) Technologies Co. Ltd Beijing China Department of Automation Tsinghua University Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

As a frequently used polymer material, polyoxymethylene (POM) has played an indispensable role in a variety of industrial fields. For production efficiency and safety, online monitoring of POM melt index is of importance in industrial practice. However, current data-driven soft sensors for POM melt index cannot address the pure delays within process variables. In this work, we propose time-delayed Takagi-Sugeno (T-S) fuzzy neural network as a novel data-driven soft sensor model, and improved quantum genetic algorithm (IQGA) for parameter estimation. By considering time-delayed process variables, the predictive performance and interpretability of soft sensor models can be improved. However, it is computationally cumbersome to find an optimal choice of time delays, and thus IQGA is proposed to resolve this problem. Case studies on industrial data from a real-world POM plant illustrate the usefulness of the proposed soft sensing method for POM melt index prediction.

关键词： Adaptation models Parameter estimation Soft sensors Computational modeling Predictive models Fuzzy neural networks data models

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A data-driven Prediction Method for Lithium-ion Batteries based on Variational Bayes and Particle Filtering

A Data-Driven Prediction Method for Lithium-ion Batteries ba...

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data driven control and learning systems (ddcls)

作者： Hao Wang Yi Deng Ru Xin Hou Qie Liu School of Automation Chongqing University Chongqing

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the remaining useful life (RUL) prediction and state of health (SOH) assessment of lithium-ion batteries are the key technologies for achieving equipment prediction and health management (PHM). Accurate RUL prediction results can serve as evaluation indicators for battery management systems(BMS), ensuring their safe and stable operation. this paper proposes a model parameter estimation method based on particle filtering (PF) and variational Bayesian (VB) to address the issues of high degradation noise and small data sample size in lithium-ion batteries, which lead to large prediction model errors and poor real-time performance. Considering the impact of uncertainty in noise, this paper uses a hybrid model to describe the data degradation process of lithium-ion batteries at different time intervals and probabilistically describes the measurement noise and model parameters. On this basis, the variational Bayes method is used to perform posterior estimation on each parameter, and the particle filter optimization method is used to dynamically update the values of the model parameters. this paper conducts experimental verification using publicly available datasets from NASA. the results indicate that this method can achieve good predictive performance.

关键词： Lithium-ion batteries Degradation Uncertainty Parameter estimation Filtering Noise NASA

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A New dataset for Intrusion Detection in Industrial control System: A Gas Pipeline Testbed Study 21

A New Dataset for Intrusion Detection in Industrial Control ...

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21st ieee International Symposium on Parallel and Distributed Processing with Applications, 13th ieee International conference on Big data and Cloud Computing, 16th ieee International conference on Social Computing and Networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Deng, Longmin Zhang, Xuemin Zheng, Qianrong Zhao, Dongdong Zhou, Junwei Xiang, Jianwen Wuhan University of Technology Hubei Key Laboratory of Transportation of Internet of Things School of Computer Science and Artifical Intelligence Wuhan China Wuhan University of Technology Chongqing Research Institute Chongqing China

ISBN: (纸本)9798350329223

the security of Industrial control systems (ICS) is a critical issue that has gained increasing attention in recent years. Machine learning based intrusion detection systems (IDS) have shown promise in detecting previously unknown attacks, but their performance depends heavily on the quality of the dataset. However, most existing datasets in the field are either outdated or do not reflect the specific characteristics of ICS. We construct a dataset of ICS network traffic collected from a gas pipeline testbed, which covers a range of normal and abnormal events specific to the domain. We detail our methodology for constructing the dataset based on the gas pipeline testbed. Moreover, the systematic and fundamental comparative experiments are performed, based on the collected dataset. Specifically, we evaluate the performance of five supervised machine learning models on it while comparing their results. Our experimental results show that the dataset can serve as a valuable resource for data-driven IDS research and that Random Forest is an effective model for detecting anomalies in ICS data. © 2023 ieee.

关键词： Intrusion detection

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A Novel Time Diffusion Model for Industrial Time Series data Generation

A Novel Time Diffusion Model for Industrial Time Series Data...

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data driven control and learning systems (ddcls)

作者： Chen Zheng Xiaoyu Jiang Zhihuan Song Xinmin Zhang Bingbing Shen State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China School of Mathematics Hangzhou Normal University Hangzhou P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Given the intricate temporal correlations inherent in most process industry systems, temporal dynamic modeling methods based on time series data have demonstrated superior performance in the area of industrial data-driven modeling. However, due to constraints in data acquisition and maintenance methods, obtaining a substantial volume of high-quality and intact time series data from process industries is often challenging. To address this challenge, AI-Generated Content technologies (AIGC) can be employed to produce high-quality virtual data and ensure data sufficiency. this paper proposes a novel Time Diffusion Model (TDM) for the generation of industrial time series data. TDM introduces diffusion models to better capture the temporal correlations and data distributions of industrial time series data, enabling the generation of high-quality virtual time series data. this capability extends its applicability to time series data imputation and time series data augmentation tasks. the proposed TDM model is applied to the practical primary reformer process case. the experimental results substantiate that TDM can generate industrial time series data of high quality and effectively enhance the performance of industrial data-driven tasks.

关键词： Industries Correlation Soft sensors Time series analysis Process control Time division multiplexing Diffusion models

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data Preprocessing Method for Landslide Displacement Prediction Based on XG Boost

Data Preprocessing Method for Landslide Displacement Predict...

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data driven control and learning systems (ddcls)

作者： Shuang Luo Xinhua El Xiaoli Li Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In machine learning-driven landslide prediction models, data quality directly determines the accuracy of landslide predictions. Currently, data preprocessing methods mainly involve fitting the data using moving averages or least squares methods. While these methods are effective and convenient for processing global data, they face limitations when dealing with local data during heavy rainfall events. the sharp increase in cumulative landslide displacement during significant rainfall makes it challenging for overall data preprocessing methods to effectively handle local data, leading to potential data loss and compromising data quality. To address this limitation, this paper proposes a segmented data processing approach. During periods of heavy rainfall, cubic spline interpolation is employed to interpolate missing data, providing a better representation of data variability. For smaller rainfall amounts, linear interpolation is utilized. Finally, the LOESS smoothing method is applied to further enhance data quality. these meticulous data processing steps aim to accurately capture the changing trends in rainfall-induced landslides. By providing more reliable inputs to machine learning prediction models, this approach seeks to improve the accuracy and reliability of landslide predictions.

关键词： Interpolation Rain Smoothing methods Accuracy data integrity data preprocessing Predictive models

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Research on Blood Glucose Nonlinear controller Based on Backstepping Adaptive control Algorithm

Research on Blood Glucose Nonlinear Controller Based on Back...

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data driven control and learning systems (ddcls)

作者： Shuya Liu Ruizhuo Song Xiang Lu Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Automation and Electrical Engineering Beijing Institute of Technology Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Closed-loop control of blood glucose regulation in type 1 diabetes patients can be achieved through an artificial pancreas. In this article, a nonlinear controller based on backstepping adaptive approach is proposed for the integration of the artificial pancreas, with a focus on improving the effectiveness of postprandial blood glucose control. By extending the Bergman physiological model using a digestion kinetics equation, an extended blood glucose dynamic model is established to effectively capture the relationship between meal intake and blood glucose variations. the blood glucose model, which includes personalized unknown parameters, serves as the basis for the derivation of the control algorithm using a combination of backstepping control and adaptive control algorithms. By accurately calculating the required dosage and timing of insulin injections for individual patients, the designed control algorithm enables precise blood glucose control. the resulting control signal, capable of adapting to unknown parameters, effectively regulates fasting and postprandial blood glucose levels in type 1 diabetes patients. the effectiveness and robustness of the proposed blood glucose control algorithm are demonstrated through the implementation of various experimental protocols.

关键词： Adaptation models Backstepping Mathematical models Physiology Robustness Glucose Diabetes

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Application of CPU Parallel Computing in MATLAB Environment for Analysis of Anti-Saturation and Gain Effects in PID controller

Application of CPU Parallel Computing in MATLAB Environment ...

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data driven control and learning systems (ddcls)

作者： Ruirui Huang Zhenhui Wu Yandong Hou Qianshuai Cheng Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Technology and Media University of Henan Kaifeng Kaifeng China School of Artificial Intelligence Henan University Zhengzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

With the continuous increase in data size and model complexity, the computational workload has grown rapidly, posing a significant challenge to the capabilities of computer data processing and simulation calculations. therefore, parallel programming based on multicore and cluster architectures has become one of the mainstream technologies to enhance program execution efficiency and numerical computation efficiency. the theoretical foundations of parallel program computing have been applied in various aspects of engineering applications and theoretical simulations. In this paper, a new parallel PID anti-integral saturation controller is designed for a second-order closed-loop control system of unmanned aerial vehicles (UAVs). It compares and analyzes the runtime, execution efficiency, and speedup ratio of parallel programs and general serial programs under the same scenario. the experimental results demonstrate that parallel computing significantly improves the simulation program's efficiency for PID controller anti-saturation control systems under identical scenarios, exhibiting a high speedup ratio on the existing computing platform. Additionally, this study consolidates common issues encountered in MATLAB parallel program design, offering valuable insights into overcoming challenges in this domain.

关键词： Runtime Parallel programming Multicore processing Process control Parallel processing control systems Central Processing Unit

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A Speech Emotion Recognition System Based on Energy Valley Optimizer Improved Multi-Kernel Extreme learning Machine

A Speech Emotion Recognition System Based on Energy Valley O...

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data driven control and learning systems (ddcls)

作者： Aihua Zhang Xuanjing Zhao College of Physical Science and Technology Bohai University Jinzhou

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In response to the issue of low accuracy in speech emotion recognition, this paper proposes an improved classification model that utilizes the Energy Valley Optimizer (EVO) to enhance the Multi-kernel Extreme learning Machine (MKELM). the Multi-kernel Extreme learning Machine (MKELM) has been widely applied in classification problems, enhancing learning and generalization capabilities compared to traditional Extreme learning Machines (ELM). However, its performance is highly dependent on parameters, making parameter optimization crucial. the EVO algorithm offers fast convergence, parameter-free operation, and the lowest possible objective function evaluations, providing significant advantages in parameter optimization. the paper conducted two sets of comparative experiments. Firstly, it compared MKELM with basic networks such as BP, SVM, LSTM, CNN in terms of runtime and accuracy, demonstrating the superiority of the MKELM model. Secondly, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and EVO were utilized to optimize the MKELM model separately. the classification results were compared in terms of accuracy, precision, and recall, demonstrating the feasibility of the proposed EVO-MKELM model in speech emotion recognition.

关键词： Support vector machines Emotion recognition Accuracy Runtime Extreme learning machines Speech recognition Speech enhancement

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GOSMART: A data-driven Web Platform for Personalized Study Recommendations and Academic Performance Improvement 12

GOSMART: A Data-Driven Web Platform for Personalized Study R...

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12th ieee conference on systems, Process and control, ICSPC 2024

作者： Kasim, Nasrul Arif Bin Abu Khalip, Suhaila Binti Subbiah, Ahgalya Management and Science University Faculty of Information Science and Engineering Section 13 Selangor Shah Alam40100 Malaysia

ISBN: (纸本)9798350391398

Education has evolved significantly over the years, transitioning from traditional, one-size-fits-all models to more dynamic, personalized learning approaches. Today's students face unique challenges such as diverse learning styles, time management issues, and a need for high-quality resources. these factors impact academic performance and attrition rates, especially in rapidly growing undergraduate populations in Malaysia. this paper presents GOSmart, an innovative webbased system designed to enhance undergraduate students' study skills through personalized learning recommendations at Management and Science University. the primary objectives are to develop a system that provides personalized study recommendations, improves time management skills, and enhances overall academic performance. GOSmart integrates adaptive learning algorithms, progress tracking, and data visualization to provide tailored study recommendations. Developed using agile methodologies, the system includes a userfriendly front-end (HTML, CSS, JavaScript) and a robust back end (PHP, MySQL). A mixed-methods approach, involving structured questionnaires administered to 500 undergraduate students at Management and Science University, informed the design and feature set of the application. data analysis revealed that 75% of students experienced improved time management and study strategies, while overall academic performance increased by 25%. GOSmart not only enhances student learning experiences but also serves as a prototype for future research in personalized education technologies. © 2024 ieee.

关键词： Contrastive learning

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Dynamic Modeling of Wet FGD Process Using PACF-GRU-AM Based Method

Dynamic Modeling of Wet FGD Process Using PACF-GRU-AM Based ...

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data driven control and learning systems (ddcls)

作者： Quanbo Liu Xiaoli Li Kang Wang Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the combustion of fossil fuels has achieved a high proportion of world's electricity generation, and different types of atmospheric pollutants such as sulfur dioxide (SO 2 ) are produced during this process. As a substantial amount of SO 2 is released by coal combustion, flue gas desulfurization (FGD) technologies are extensively used in coal-fueled power plants, and the design of FGD modeling approach plays a fundamental role in optimization and control of the desulfurization process. Nevertheless, the FGD in an industrial setting is a rather complicated process which has characteristics of non-linearity, time delays and non-stationarity, rendering dynamic learning of an FGD process a truly formidable problem. In this research, a novel FGD modeling approach integrating partial autocorrelation function (PACF), gated recurrent unit (GRU) neural network and attention mechanism (AM) was proposed to make a prediction of the limestone slurry pH. the effectiveness of the proposed modeling system is verified through the investigation of a real FGD process, besides the structural flexibility and outstanding performance of our method demonstrates its widespread application prospects in different industrial scenarios.

关键词： Analytical models Attention mechanisms Atmospheric modeling Predictive models data models Combustion Delays

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